Raw data VR Acceptance
Bibliographic record
Abstract
These files contain the raw data of a study on the attitudes of older adults towards head-mounted immersive virtual reality. Sixty participants completed a scale measuring attitudes (Att) towards head-mounted virtual reality, computer proficiency (CP) and computer self-efficacy (CSE) in a first test session. In addition, the Montreal Cognitive Assessment (MoCA) and the praxis subscale of the Birmingham Cognitive Screen (BCoSPraxis) were administered to participants to measure cognitive status and praxis. Using these data we tested whether initial attitudes depend on age, years of formal education and computer proficiency. In addition, 37 participants were exposed to a first HMD-VR user experience and 22 control participants were exposed to content-matched time-lapse videos. We evaluated whether attitudes towards HMD-VR changed more strongly in the HMD-VR versus the control group. In addition, we also measured the experience of the HMD-VR exposure or time-lapse videos, symptoms of cybersickness (SSQ) after each experience and the tendency to answer in a socially desirable fashion (SDS). Moreover, we also measured the openness personality trait using a short form of the Neuroticism Extraversion Openness Inventory (NEO). The data were collected in 3 test phases: phase 3, 4, 5 represent the participants of the HMD-VR group, while test phase 7 represents the control group. The demographic data (Dem) in the shared dataset were adjusted by removing variables that are unnecessary to replicate our results and that may identify individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.438 | 0.180 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".